PointNet [1]} solves the problem of order invariance caused by simple deep neural network (DNN) models by directly processing 3D point clouds. The architecture consists of three main components: max-pooling layers as symmetry function, shared multi-layer perceptrons (MLPs), and a local-global information fusion module to classify and segment 3D point clouds. However, this network cannot describe shape. PointNet++ [2]} first partitions points set into overlapping local regions and then produces higher-level features by extracting, grouping, and processing the local features.
